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As the prerequisite of non-destructively measuring structural parameters and leaf distribution, wood-leaf separation plays an essential role in forest inventories. However, fewer studies focus on the separation and measurement of large tropical trees with heavy crowns and complex branch structures. This study proposed a novel method to automatically separate the leaf and wood points of large tropical trees based on geometric features. Instead of identifying all wood points using the same rules, we used different methods to separate small and large wood components, respectively. The identification of small wood components was implemented mainly by the differences in point density and linear distribution pattern between leaf and wood points, while the identification of large wood components was implemented through the comprehensive analysis of verticality, linearity, anisotropy, as well as point density. To improve the separation accuracy and implementation effectiveness, the segment-wise and point-wise methods were combined in this study. The robustness and generalization of the proposed method were tested using two datasets, i.e. twenty large tropical trees with heavy crowns and twenty-four general tropical trees without heavy crowns. The separation results verified that the proposed method could achieve good separation of wood and leaf points of large crown-heavy tropical trees with the accuracy of up to 91.5%. The highest separation accuracy of general tropical trees was about 95.03%. The examination of the tropical trees without heavy crowns demonstrated that the proposed method has promising robustness and generalization ability. In addition, to fill the gap in the large tropical tree point clouds, an open-source dataset library was built for the wood-leaf separation research, including manually labelled 20 large crown-heavy tropical trees with different types of branch structures and basic structural parameters of each tree (tree height, crown spread, and diameter at the breast height (DBH)). [ABSTRACT FROM AUTHOR]

Previous research on university students' academic performance indicates that students' seating locations can be an indicator of their academic performance. However, previous research on the relationship between university students' seating locations and their academic performance is mixed, and there is a lack of research about whether students' academic discipline moderates this relationship. This study addresses this gap in the literature through a study of 182 university students in Hong Kong. Biglan categorization was used to classify disciplines into soft (a low degree of consensus) and hard fields (a high degree of consensus). The results revealed that students from soft fields who sat at the front of a class performed better than their counterparts who sat at the back, while the performance of students from hard fields was not significantly influenced by their seating location. This article's findings suggest that classroom seating arrangements should be tailored to academic disciplines to maximize their efficacy and that universities should redesign classroom layouts to extend the experience of sitting at the front of a class to all students. [ABSTRACT FROM AUTHOR]

The urban heat island (UHI) phenomenon occurring within urban areas or city-clusters is increasingly becoming a severe problem in the urbanization process. Previous research mainly focusing on static UHI modeled at fixed time instants is not capable to track the evolutionary process of the UHIs in both time and space domains. This research designs an object-oriented dynamic model to reconstruct the evolutionary process of UHIs. Each UHI is modeled as a spatiotemporal field-object with its own life-cycle. The dynamic behavior of an UHI is defined by a series of filiations (e.g. expansion and contraction). The model was implemented in an object-relational database and applied to air temperature data collected from weather stations in hourly basis over 7 days. The behaviors of UHI were extracted from the data. Results suggest that the model can effectively identify different behaviors and status of UHIs, and reveal the spatiotemporal behavior of each of them. [ABSTRACT FROM AUTHOR]

Aerosol observations are essential for understanding the Earth's radiation budget and the complexities of climate change, as they are involved in the backscattering of solar radiation and the formation of cloud condensation nuclei. In Hong Kong, the most direct effect is on air quality. Atmospheric haze caused by the emission of aerosols from industrial and vehicular sources creates visibility lower than 8 km for approximately 20% of the time, having risen at 6% per decade since 1980, but regional emissions are at least as influential as local ones. The 179,000 km2 covered by Hong Kong and neighbouring Guangdong Province cannot be adequately covered by the 76 monitoring stations set up by the two governments, and satellite images offer the only potential source of regional air quality data. However, the current satellite-based aerosol optical thickness (AOT) products are intended for global air quality monitoring, and may contain errors over a humid coastal city such as Hong Kong and its surrounding industrialized regions. This research compares the AOT retrieved from several AOT operational products, namely the Moderate Resolution Imaging Spectroradiometer (MODIS) MOD04 product, the MODIS 500 m product, the Multiangle Imaging Spectroradiometer (MISR) product, the Ozone Monitoring Instrument (OMI) multiwavelength aerosol product, and the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) product, with ground-based AOT from sunphotometers in Hong Kong. These sunphotometers include two AERONET stations, and are deployed in Hong Kong over urban, suburban, and coastal areas. The rigorous correlations, root mean square errors, and mean absolute differences available from the multilocational field data within one city region provide a strong base for validating the AOT products from different sensors and at different spatial scales over different land surface types. The results suggest that the AOT products, especially those from MODIS 10 km, provide reliable and accurate observations for daily air quality monitoring over a variety of land-cover types, as well as for identifying emission sources for coordinated actions by the governments of Hong Kong and the Chinese mainland. [ABSTRACT FROM PUBLISHER]

In densely urbanized regions, the local climate is greatly influenced by the urban morphology, including interactions between buildings, space, and human activities. The Kowloon Peninsula in Hong Kong, with some of the greatest urban population densities in the world, represents an extreme case of the influence of the built environment on climate, with high-rise buildings, narrow street canyons, and little green space. In this study, the building frontal area index (FAI), a parameter for estimating aerodynamic resistance of the urban surface as a predictor of wind ventilation, was derived from a three-dimensional building database. The relationship between FAI and urban heat island intensity (UHII) from an Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) satellite image was calculated at different scales. The highest correlation (r = 0.574, n = 4900) was obtained at 100 m resolution, which suggests that the optimum operational scale of FAI is 100 m resolution for the study area, i.e. the scale and size at which FAI impacts on the urban climate. The presence/trend of any higher correlation at different resolutions was tested using the nonparametric Mann–Kendall test, and the results show that the statistic Z-value generated from the test is smaller than the hypothesis significance levels of 90%, 95%, and 99%; thus, the hypothesis of having a higher correlation at any scale other than 100 m resolution is rejected. Planning authorities may use the FAI generated at 100 m resolution for designing wind ventilation corridors across Hong Kong at this scale, especially when temperature and air quality in the inner city are of major concern. However, for applications to other cities with different standard morphologies, the FAI–UHII relationship should be re-evaluated. [ABSTRACT FROM PUBLISHER]

Hong Kong, located adjacent to the rapidly growing urban-industrial region of south China, provides a case of mixed aerosol types (urban, industrial, marine, and long-distance, including dust) from diverse activities and has suffered many serious air pollution episodes over the last decade. However, the sources and transport pathways of aerosols measured and recorded in Hong Kong have not been well researched due to the lack of air quality monitoring stations in east Asia. Here, an integrated method combining Aerosol Robotic Network (AERONET) data, backward trajectories, and Potential Source Contribution Function (PSCF) modelling is used to identify probable transport pathways and magnitudes of source contributions for four characteristic aerosol types. These types, which are dominant in Hong Kong during defined climatic and environmental conditions, are urban fine aerosols, urban mixed aerosols, dust, and heavy pollution. They were defined by clustering a total of 730 AERONET data sets between 2005 and 2008. Results show that aerosol types 1 and 2 (urban fine and urban mixed) are associated with regional fine particulate urban emissions and predominantly local urban emissions, respectively, suggesting that mitigating measures taken within Hong Kong itself would be partially effective. Heavy pollution and dust (types 4 and 3) are more associated with short- and long-distance sources, notably heavy industries in nearby southern Guangdong and the Pearl River Delta region, and desert dust from arid regions in north China. The PSCF map representing dust aerosol type shows a wide range of eastward and southeastward trajectories from northwest China to Hong Kong. Although the contribution of dust sources is small compared to anthropogenic aerosols, a serious recent dust outbreak observed in Hong Kong was associated with an elevation of the air pollution index to 500, compared with 50–100 on normal days. The combined use of clustered AERONET, backward trajectories, and PSCF model can help to resolve long-standing issues about source regions and characteristics of pollutants carried to Hong Kong. [ABSTRACT FROM PUBLISHER]

This study investigates the relationship between aerosol optical thickness (AOT) derived from MODerate resolution Imaging Spectroradiometer (MODIS) satellite and in situ particulate matter (PM2.5) from Hong Kong air-quality monitoring stations. The relationship was analysed for three different AOT products, namely, MODIS collection 5 AOT data, MODIS collection 5 fine-mode fraction AOT data, both at 10 km resolution, and MODIS AOT data at 500 m resolution. In view of the predicted low accuracies obtainable for MODIS AOT products for the south China region, these AOT products were first validated against AOT measurements from an AErosol RObotic NETwork (AERONET) station near the centre of Hong Kong. Strong relationships of R 2 = 0.78 and R 2 = 0.77 for the 10 km and 500 m AOT data, respectively, were obtained, thus providing a robust AOT image database at both coarse and fine spatial resolution for comparison with PM2.5 concentrations. When a whole year (2007) of AOT images was compared with PM2.5 concentrations recorded at five ground stations, correlations of R 2 = 0.31, R 2 = 0.10 and R 2 = 0.67 were obtained for collection 5, fine-mode fraction of collection 5 (both at 10 km resolution) and 500 m AOT, respectively. Strong correlations between MODIS 500 m AOT and PM2.5 concentration were also observed for individual stations (R 2 = 0.66, 0.74, 0.76, 0.56 and 0.62, for Central, Tung Chung, Tseun Wan, Yuen Long and Tap Mun stations, respectively). The study suggests that fine particle distributions at a high level of detail over whole cities may be obtained from satellite images. Since the model has potential for further refinement, monitoring of detailed PM2.5 concentrations on a routine basis from satellite images will provide a highly useful tool for urban environmental authorities. [ABSTRACT FROM AUTHOR]

Hong Kong is one of the world's mountainous international cities, and landslides are a constant threat to human life and property. Monitoring landslides in Hong Kong is important and this is always done by field surveying. However, although conventional survey techniques provide accurate landslide information, they are limited to small areas and physical contact with the slope may be dangerous. Remote sensing techniques can provide an alternative for collecting information about landslide causes and occurrences, and they may assist in the prediction of future landslide occurrences. This article demonstrates the use of monoscopic and stereoscopic aerial photographs, along with satellite images from the IKONOS very high resolution (VHR) sensor for detailed landslide hazard assessment over Hong Kong. For monoscopic aerial photographs, a fusion technique for generating pseudo true colour images from false colour aerial photographs was demonstrated. The pseudo true colour image is useful for better visual analysis in the photogrammetric model. For monoscopic IKONOS image, a set of image fusion techniques was applied in order to improve landslide interpretation, and the results were examined visually and statistically. The Pan-sharpening method among all the image fusion techniques has been demonstrated to have superior performance for identifying both landslide trails and crowns. Stereoscopic viewing using a stereoscopic pair of aerial photographs and stereoscopic IKONOS images was employed for more detailed landslide investigation such as landscape positional relationships (e.g. streams and ridges). Digital elevation models (DEM) were generated from aerial photographs and IKONOS stereoscopic images, and they were compared with digital contour data with 2 m contour interval. The DEMs generated from digital photogrammetric model and IKONOS stereoscopic images are consistently more accurate than an existing DEM, and are sensitive to micro-scale terrain features. The Hong Kong Civil Engineering Department may use the derived monoscopic fused images, stereoscopic images, DEM and anaglyph as objective measures for a detailed landslide study within Hong Kong. [ABSTRACT FROM AUTHOR]

Observations from the AErosol RObotic NETwork (AERONET) sunphotometers, MODerate resolution Imaging Spectroradiometer (MODIS) satellite images, back-trajectory modelling and 'in-situ' PM10 measurements in Hong Kong confirmed that two dust storms on 16-17 April 2006 and 27-30 April 2009, with source areas in northwest China, affected the city. The impacts of the dust on the air quality of Hong Kong were quantified using aerosol optical properties from AERONET data and local PM10 (particle size less than 10 μm) concentrations. Combined analysis of back trajectories and the microphysical properties of the dust aerosols from AERONET inversion data suggest that the dust particulates are sometimes associated with industrial chemicals on arrival in Hong Kong. This is the first remote-sensing study to observe the presence and characteristics of Asian dust carried into the humid tropical region of south China. [ABSTRACT FROM AUTHOR]